feat(briefing): Add python fallback for empty data

api/main.py

@@ -427,7 +427,7 @@ async def generate_outline_route(request: OutlineRequest):
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"An internal error occurred in the AI model: {e}")
     
-    
+
 @app.post("/generate-dashboard-insights", response_model=StrategyResponse, summary="Generate Insights from Dashboard KPIs")
 @cached_response
 def generate_dashboard_insights_route(request: DashboardInsightsRequest):
@@ -1308,41 +1308,49 @@ You are a social media expert. Analyze the following caption... Respond ONLY wit
 @app.post("/generate/daily-briefing", response_model=DailyBriefingResponse, summary="Generates a daily action plan for the Talent Manager")
 def generate_daily_briefing(data: DailyBriefingData):
     """
-    Takes various KPIs from the backend, synthesizes them, and uses the LLM
-    to generate a short, actionable daily briefing for a Talent Manager.
+    [BULLETPROOF VERSION] Takes KPIs and uses either the LLM (if data exists) or
+    Python logic (if data is empty) to generate a daily briefing.
     """
-    print(f"\n✅ Received request on /generate/daily-briefing")
+    print(f"\n✅ Received request on /generate/daily-briefing with data: {data}")
+    
+    # === THE FINAL, BULLETPROOF FIX IS HERE ===
+    on_bench = data.on_bench_influencers
+    pending_tasks = data.pending_submissions + data.revisions_requested
+
+    # SAFETY CHECK: Agar koi important data nahi hai, toh AI ko call mat karo.
+    # Python se hi ek accha, static message bhejo.
+    if on_bench == 0 and pending_tasks == 0:
+        print("   - ✅ No critical tasks found. Returning Python-generated 'All Clear' message.")
+        return DailyBriefingResponse(
+            briefing_text="All clear! No urgent actions are required. Your roster is fully engaged and up-to-date."
+        )
+    # === END OF FIX ===
+        
     if not _llm_instance:
         raise HTTPException(status_code=503, detail="The Llama model is not available for briefing.")
 
-    # --- ✨ THE FINAL "IDIOT-PROOF" PROMPT FOR TINYLLAMA ---
     final_prompt = f"""
 Summarize these key points into 2-3 direct bullet points for a manager.
 
 DATA:
-- Influencers without campaigns: {data.on_bench_influencers}
-- Submissions needing review: {data.pending_submissions + data.revisions_requested}
+- Influencers without campaigns: {on_bench}
+- Submissions needing review: {pending_tasks}
 - Total pending money: {data.highest_pending_payout:,.0f} INR
 
 SUMMARY:
 - """
     
     try:
-        print("--- Sending briefing data to LLM (Idiot-Proof prompt)...")
-        # Temperature 0.1 karne se model aur zyada factual aur kam creative hoga
+        print("--- Sending briefing data to LLM (Data exists)...")
         response = _llm_instance(final_prompt, max_tokens=150, temperature=0.1, stop=["DATA:"], echo=False)
-        
         briefing_text = response['choices'][0]['text'].strip()
 
-        # Add our own header to make it look nice
         final_briefing = f"Here are your top priorities for today:\n- {briefing_text}"
-
-        print("--- Successfully generated daily briefing.")
+        print("--- Successfully generated AI briefing.")
         return DailyBriefingResponse(briefing_text=final_briefing)
 
     except Exception as e:
         print(f"🚨 An unexpected error occurred during briefing generation:")
-        import traceback
         traceback.print_exc()
         raise HTTPException(status_code=500, detail="Failed to generate AI briefing.")